Model-based variance estimation in two-dimensional systematic sampling

A variance estimator for the mean of a systematic sample in two dimensions is proposed and analyzed. The estimation strategy relies on a super-population model which follows a spatial auto-regressive structure and allows for the presence of covariates. The small sample properties of the proposed procedure are analyzed by simulations: the model-based estimation strategy shows an excellent performance in a variety of situations which are common in real situations.